Knowledge-grounded Natural Language Recommendation Explanation
This addresses the need for objective and reliable explanation generation in recommendation systems to enhance user trust, though it is incremental as it builds on existing knowledge graph and collaborative filtering methods.
The paper tackles the problem of generating natural language explanations for recommendations by proposing a knowledge graph approach that uses item features and user purchase history to create fact-grounded, personalized explanations, and it shows consistent outperformance over previous state-of-the-art models in experiments.
Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user's preferences, based on the user's purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation.